semnan-university-ai / dermatology

Dermatology Data Set

# Author : Amir Shokri
# github link : https://github.com/amirshnll/dermatology
# dataset link : http://archive.ics.uci.edu/ml/datasets/Dermatology
# email : [e-mail]

import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score
from sklearn.metrics import accuracy_score

data = pd.read_csv('dermatology_data.csv', header=None)

data = data.replace(to_replace="?", method='ffill')

data.describe()

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	0	1	2	3	4	5	6	7	8	9	...	24	25	26	27	28	29	30	31	32	34
count	366.000000	366.000000	366.000000	366.000000	366.000000	366.000000	366.000000	366.000000	366.000000	366.000000	...	366.000000	366.000000	366.000000	366.000000	366.000000	366.000000	366.000000	366.000000	366.000000	366.000000
mean	2.068306	1.795082	1.549180	1.366120	0.633880	0.448087	0.166667	0.377049	0.614754	0.519126	...	0.393443	0.464481	0.456284	0.953552	0.453552	0.103825	0.114754	1.866120	0.554645	2.803279
std	0.664753	0.701527	0.907525	1.138299	0.908016	0.957327	0.570588	0.834147	0.982979	0.905639	...	0.849406	0.864899	0.954873	1.130172	0.954744	0.450433	0.488723	0.726108	1.105908	1.597803
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000
25%	2.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	1.000000
50%	2.000000	2.000000	2.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000	0.000000	3.000000
75%	2.000000	2.000000	2.000000	2.000000	1.000000	0.000000	0.000000	0.000000	1.000000	1.000000	...	0.000000	1.000000	0.000000	2.000000	0.000000	0.000000	0.000000	2.000000	0.000000	4.000000
max	3.000000	3.000000	3.000000	3.000000	3.000000	3.000000	3.000000	3.000000	3.000000	3.000000	...	3.000000	3.000000	3.000000	3.000000	3.000000	3.000000	3.000000	3.000000	3.000000	6.000000

8 rows × 34 columns

properties = data[data.columns[:34]]
target = data[data.columns[34]]
scaler = MinMaxScaler()
scaled_x = scaler.fit_transform(properties)

target.value_counts().plot.pie()

<matplotlib.axes._subplots.AxesSubplot at 0x23b944ca7f0>

pca = PCA(n_components=15)
reduced_x = pca.fit_transform(scaled_x)

X_train, X_test, y_train, y_test = train_test_split(reduced_x, target, test_size=0.3, random_state=0)

from sklearn.naive_bayes import GaussianNB
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression

gnb = GaussianNB()
mlp = MLPClassifier(hidden_layer_sizes=(100, 100))
knn = KNeighborsClassifier(n_neighbors=5)
dt = DecisionTreeClassifier()
regressor = LogisticRegression()

gnb.fit(X_train, y_train)
y_predgnb = gnb.predict(X_test)

mlp.fit(X_train, y_train)
y_predmlp = mlp.predict(X_test)

knn.fit(X_train, y_train)
y_predknn = knn.predict(X_test)

dt.fit(X_train, y_train)
y_preddt = dt.predict(X_test)

regressor.fit(X_train, y_train)
y_predregressor = regressor.predict(X_test)

C:\Users\Amirshnll\anaconda3\lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(

print('gnb f1: ', f1_score(y_test, y_predgnb, average='micro'))
print('gnb accuracy: ', accuracy_score(y_test, y_predgnb))

print('mlp f1: ', f1_score(y_test, y_predmlp, average='micro'))
print('mlp accuracy: ', accuracy_score(y_test, y_predmlp))

print('knn f1: ', f1_score(y_test, y_predgnb, average='micro'))
print('knn accuracy: ', accuracy_score(y_test, y_predknn))

print('decision tree f1: ', f1_score(y_test, y_predgnb, average='micro'))
print('decision tree accuracy: ', accuracy_score(y_test, y_preddt))

print('logistic regression f1: ', f1_score(y_test, y_predgnb, average='micro'))
print('logistic regression accuracy: ', accuracy_score(y_test, y_predregressor))

gnb f1:  0.9727272727272728
gnb accuracy:  0.9727272727272728
mlp f1:  1.0
mlp accuracy:  1.0
knn f1:  0.9727272727272728
knn accuracy:  0.9818181818181818
decision tree f1:  0.9727272727272728
decision tree accuracy:  0.9090909090909091
logistic regression f1:  0.9727272727272728
logistic regression accuracy:  0.990909090909091